A note on the applied use of MDL approximations

Authors:
Daniel J. Navarro
Affiliations:
Department of Psychology, Ohio State University, Columbus, OH
Venue:
Neural Computation
Year:
2004

Citing 4
Cited 2

Statistical inference, Occam's razor, and statistical mechanics on the space of probability distributions

Neural Computation
Monte Carlo Statistical Methods (Springer Texts in Statistics)

Monte Carlo Statistical Methods (Springer Texts in Statistics)
Fisher information and stochastic complexity

IEEE Transactions on Information Theory
Strong optimality of the normalized ML models as universal codes and information in data

IEEE Transactions on Information Theory

MDL denoising revisited

IEEE Transactions on Signal Processing
False alarm rate estimation for information-theoretic-based source enumeration methods

EURASIP Journal on Advances in Signal Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

An applied problem is discussed in which two nested psychological models of retention are compared using minimum description length (MDL). The standard Fisher information approximation to the normalized maximum likelihood is calculated for these two models, with the result that the full model is assigned a smaller complexity, even for moderately large samples. A geometric interpretation for this behavior is considered, along with its practical implications.